Research fellow in "Smart Technologies"

University of Stavanger
October 12 2017
Position Type
Full Time
Organization Type

The University of Stavanger invites applications for a doctorate scholarship in “Smart Technologies” at Department of Computer Science and Electrical Engineering.

This is a trainee position that will give promising researchers an opportunity for academic development leading to a doctoral degree.

Appointment is for three years with research duties. The position is vacant from January 2018 or by appointment.

This project is in the area of machine learning and deep learning with applications to text/news mining, tracking and summarizing real-time events and social media. The goal is to develop machine learning and deep neural models for generating holistic summaries of multi-source events for smart technology applications.

The event data can be from variety sources such as news, social media as well as city monitoring sensors such as traffic, weather forecast and pollution monitoring etc. This massive-scale data is only beneficial when application-specific actionable knowledge is extracted from them. For example, summarizing people's reactions on the topics related to search and rescue during natural disasters along with correlated traffic and weather situation may guide the emergency services in smart cities to manage the distribution of resources and supplies more effectively.

Deep Neural Networks (DNNs) have recently been very successful for performing several difficult learning tasks with high precision for applications in several domains such as computer vision, natural language processing and machine translation etc. Although, DNNs work well when large-scale labelled training data is available, their application to generate target-specific holistic event summaries has never been tried before. Using DNNs to learn from input sequences in the form of event streams to general summary sequences as output is a challenging open problem.

The position is funded by Norwegian Ministry of Education and Research.

Applicants must have a strong academic background with a five-year degree (master + bachelor) within Computer Science, or in a related study, preferably recently, or possess corresponding qualifications, which could provide a basis for successfully completing a doctorate. Both the grade for the master's thesis and the weighted average grade of the master's degree must individually be equivalent to or better than a ‘B'.

In evaluating the applicants, emphasis will be placed on their potential for research in the field.

The appointee must be able to work independently and as a member of a team, be creative and innovative.

The research fellow must have a good command of both oral and written English.

The resulting PhD degree will qualify for research and teaching positions at University level.

The appointee will be based at the University of Stavanger, with the exception of a stay abroad at a relevant center of research. 

The research fellow is salaried according to the State Salary Code, 17.515, code 1017, LR 20, ltr. 50 of NOK 436 900 per annum.

The position provides for automatic membership in the Norwegian Public Service Pension Fund, which guarantees favorable retirement benefits. Members may also apply for home investment loans at favorable interest rates.

Project description and further information about the position can be obtained from Vinay Setty, email: or from Tom Ryen, telephone: +47 5183 2029, email: 

Information about the appointment procedures can be obtained from Anne Karin Rafos, telephone: +47 5183 1711, email:

The University is committed to a policy of equal opportunity in its employment practices. The University currently employs few female research fellows within this academic field and women are therefore particularly encouraged to apply.

Please register your application in an electronic form on Relevant education and experience must be registered on the form. Certificates/diplomas, research motivation letter, CV, references, list of publications and other documentation that you consider relevant, should be submitted as attachments to the application as separate files. If the attachments exceed 30 MB altogether, they will have to be compressed before uploading.

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